Radiation dose efficiency of dual-energy CT benchmarked against single-source, kilovoltage-optimized scans
Bibliographic record
Abstract
OBJECTIVE: This study evaluated the radiation dose and image quality implications of dual-energy CT (DECT) use, compared with kilovoltage-optimized single-source/single-energy CT (SECT) on a dual-source Siemens Somatom(®) Definition Flash CT scanner (Siemens Healthcare, Forcheim, Germany). METHODS: With equalized radiation dose (volumetric CT dose index), image noise (standard deviation of CT number) and signal-difference-to-noise ratio (SDNR) were measured and compared across three techniques: 100, 120 and 100/140 kVp (dual energy). Noise in a 30-cm-diameter water phantom and SDNR within unenhanced soft-tissue regions of a small adult (50 kg/165 cm) anthropomorphic phantom were utilized for the assessment. RESULTS: Water phantom image noise decreased with DECT compared with the lower noise SECT setting of 120 kVp (p = 0.046). A decrease in SDNR within the anthropomorphic phantom was demonstrated at 120 kVp compared with the SECT kilovoltage-optimized setting of 100 kVp (p = 0.001). A further decrease in SDNR was observed for the DECT technique when compared with 120 kVp (p = 0.01). CONCLUSION: On the Siemens Somatom Definition Flash system (Siemens Healthcare), and for equalized radiation dose conditions, image quality expressed as SDNR of unenhanced soft tissue may be compromised for DECT when compared with kilovoltage-optimized SECT, particularly for smaller patients. ADVANCES IN KNOWLEDGE: DECT on a dual-source CT scanner may require a radiation dose increase to maintain unenhanced soft-tissue contrast detectability, particularly for smaller patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".